U.S. patent number 7,328,104 [Application Number 11/383,895] was granted by the patent office on 2008-02-05 for systems and methods for improved inertial navigation.
This patent grant is currently assigned to Honeywell International Inc.. Invention is credited to Robert H. Fall, Lisa M. Overstreet.
United States Patent |
7,328,104 |
Overstreet , et al. |
February 5, 2008 |
**Please see images for:
( Certificate of Correction ) ** |
Systems and methods for improved inertial navigation
Abstract
A method for producing inertial measurement data is provided.
The method comprises receiving raw inertial measurement data from
one or more inertial sensors; receiving raw position data based on
signals from a global navigation satellite system; processing the
raw inertial measurement data and the raw position data with a
filter to generate state variable estimates; and calculating
enhanced inertial measurement data based on the raw inertial
measurement data and the state variable estimates from the
filter.
Inventors: |
Overstreet; Lisa M.
(Clearwater, FL), Fall; Robert H. (St. Petersburg, FL) |
Assignee: |
Honeywell International Inc.
(Morristown, NJ)
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Family
ID: |
38224764 |
Appl.
No.: |
11/383,895 |
Filed: |
May 17, 2006 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20070271037 A1 |
Nov 22, 2007 |
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Current U.S.
Class: |
701/472; 244/3.2;
318/582; 342/357.3; 701/509; 73/1.78 |
Current CPC
Class: |
G01S
19/47 (20130101) |
Current International
Class: |
G01C
21/00 (20060101) |
Field of
Search: |
;701/207,220,213,214
;73/178,1.77,1.78,1.38 ;342/357.14,357.15 ;318/582 ;340/988 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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1120665 |
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Jan 2001 |
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EP |
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0022452 |
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Apr 2000 |
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WO |
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Other References
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pp. 905-918.quadrature..quadrature.. cited by examiner .
Robust positioning technique in low-cost DR/GPS for land
navigation.quadrature..quadrature.Seong Yun Cho; Wan Sik
Choi;.quadrature..quadrature.Instrumentation and Measurement, IEEE
Transactions on.quadrature..quadrature.vol. 55, Issue 4, Aug. 2006
pp. 1132-1142.quadrature..quadrature.. cited by examiner .
Rapid development of tightly-coupled GPS/INS
systems.quadrature..quadrature.Knight,
D.T.;.quadrature..quadrature.Aerospace and Electronc Systems
Magazine, IEEE.quadrature..quadrature.vol. 12, Issue 2, Feb. 1997
pp. 14-18. cited by examiner .
GPS/INS uses low-cost MEMS IMU.quadrature..quadrature.Brown,
A.K.;.quadrature..quadrature.Aerospace and Electronic Systems
Magazine, IEEE.quadrature..quadrature.vol. 20, Issue 9, Sep. 2005
pp. 3-10.quadrature..quadrature.. cited by examiner.
|
Primary Examiner: Black; Thomas
Assistant Examiner: Peche; Jorge O
Attorney, Agent or Firm: Fogg & Powers LLC
Government Interests
GOVERNMENT LICENSE RIGHTS
The U.S. Government may have certain rights in the present
invention as provided for by the terms of Contract No.
W9113M-05-C-0026 with the US Army/Missile Defense Agency.
Claims
What is claimed is:
1. A method for producing inertial measurement data, the method
comprising: receiving raw inertial measurement data from one or
more inertial sensors; receiving raw position data based on signals
from a global navigation satellite system; processing the raw
inertial measurement data and the raw position data with a filter
to generate state variable estimates; and calculating enhanced
inertial measurement data based on the raw inertial measurement
data and the state variable estimates from the filter; and wherein
processing the raw inertial measurement data and the raw position
data with a filter to generate state variable estimates comprises
generating state variable estimates including .delta..DELTA.v and
.delta..DELTA..theta..
2. The method of claim 1, wherein receiving raw inertial
measurement data from one or more inertial sensors comprises
receiving instrument data from one or more of a gyroscope and an
accelerometer.
3. The method of claim 1, wherein receiving raw inertial
measurement data from one or more inertial sensors comprises
receiving changes in angular position (.DELTA..theta.) data and
change in velocity (.DELTA.v) data.
4. The method of claim 1, wherein receiving raw position data based
on signals from a global navigation satellite system comprises
receiving signals from one or more of a global positioning system
satellite and a Galileo positioning system satellite.
5. The method of claim 1, wherein processing the raw inertial
measurement data and the raw position data with a filter to
generate state variable estimates further comprises generating
state variable estimates including a position (P.sub.EST) and a
rate of change in position ({dot over (P)}.sub.EST).
6. The method of claim 1, wherein the enhanced inertial measurement
data provides inertial measurement data in the form of incremental
velocity change and incremental angle change information.
7. A global navigation satellite system enhanced inertial
measurement unit, the unit comprising: one or more inertial sensors
adapted to detect a change in angular position (.DELTA..theta.) and
a change in velocity (.DELTA.v) and generate raw inertial
measurement data based on the detected change in angular position
and change in velocity; a global navigation satellite system
receiver adapted to generate raw position data based on signals
received from a global navigation satellite system; a filter
coupled to the one or more inertial sensors and the global
navigation satellite system receiver, the filter adapted to
calculate one or more estimated state variables including a change
to the change in velocity (.delta..DELTA.v) and a change in the
change in angular position (.delta..DELTA..theta.) based on the raw
inertial measurement data and the raw position data; and an output
generator coupled to the one or more inertial sensors and the
filter and adapted to calculate an enhanced inertial measurement
data based on the sum of the raw inertial measurement data and the
one or more estimated state variables.
8. The unit of claim 7, wherein the inertial sensors comprise one
or more gyroscopes and accelerometers that produce the raw inertial
measurement data.
9. The unit of claim 7, wherein the global navigation satellite
system receiver comprises one or both of a global positioning
system (GPS) receiver and a Galileo positioning system
receiver.
10. The unit of claim 7, wherein the output generator is further
adapted to calculate an enhanced inertial velocity measurement
(.DELTA.v') based on the sum of the change in velocity (.DELTA.v)
and the change to the change in velocity (.delta..DELTA.v), and
calculate and enhanced inertial angular measurement
(.DELTA..theta.') based on the sum of the change in angular
position (.DELTA..theta.) and the change in the change in angular
position (.delta..DELTA..theta.).
11. The unit of claim 7, wherein the filter further calculates an
estimated position (P.sub.EST), and an estimated rate of change in
position ({dot over (P)}.sub.EST).
12. The unit of claim 11, wherein the output generator receives the
estimated position (P.sub.EST) and an estimated rate of change in
position ({dot over (P)}.sub.EST), and calculates a lag associated
with the filter, wherein the output generator is further adapted to
adjust the enhanced inertial measurement data based on the
calculated lag.
13. The unit of claim 7, wherein the output generator is adapted to
smooth the one or more estimated state variables to compensate for
differences in a data output rate of the one or more inertial
sensors and an update frequency of the filter.
14. A navigation system, the system comprising: means for
generating raw inertial measurement data; means for generating raw
position data; means for generating state variable estimates
responsive to the means for generating raw inertial measurement
data and the means for generating raw position data; and means for
calculating enhanced inertial measurement data responsive to the
means for generating raw inertial measurement data and the means
for generating state variable estimates; and wherein the means for
generating state variable estimates generates state variable
estimates including one or more of a change to the change in
velocity (.delta..DELTA.v), a change in the change in angular
position (.delta..DELTA..theta.), position (P.sub.EST) and a rate
of change in position ({dot over (P)}.sub.EST).
15. The system of claim 14, wherein the means for generating raw
inertial measurement data outputs data in the form of changes in
angular position (.DELTA..theta.) data and change in velocity
(.DELTA.v) data.
16. The system of claim 14, wherein the means for calculating
enhanced inertial measurement data is adapted to receives the
estimated position (P.sub.EST) and an estimated rate of change in
position ({dot over (P)}.sub.EST), and calculate a lag associated
with the means for generating state variable estimates, wherein the
means for calculating enhanced inertial measurement data is further
adapted to adjust the enhanced inertial measurement data based on
the calculated lag.
17. A navigation system, the system comprising: means for
generating raw inertial measurement data; means for generating raw
position data; means for generating state variable estimates
responsive to the means for generating raw inertial measurement
data and the means for generating raw position data; and means for
calculating enhanced inertial measurement data responsive to the
means for generating raw inertial measurement data and the means
for generating state variable estimates; and wherein the means for
calculating enhanced inertial measurement data generates data in
the form of a change in angular position (.DELTA..theta.') and a
change in velocity (.DELTA.v').
18. A navigation system, the system comprising: means for
generating raw inertial measurement data; means for generating raw
position data; means for generating state variable estimates
responsive to the means for generating raw inertial measurement
data and the means for generating raw position data; and means for
calculating enhanced inertial measurement data responsive to the
means for generating raw inertial measurement data and the means
for generating state variable estimates; and wherein the means for
calculating enhanced inertial measurement data is further adapted
to calculate an enhanced inertial velocity measurement (.DELTA.v')
based on a sum of the a change in velocity (.DELTA.v) and a change
to the change in velocity (.delta..DELTA.v), and calculate an
enhanced inertial angular measurement (.DELTA..theta.') based on a
sum of a change in angular position (.DELTA..theta.) and a change
in the change in angular position (.delta..DELTA..theta.).
Description
BACKGROUND
Many large scale military and commercial navigation systems use
software to arrive at navigation solutions. Inertial data from
inertial measurement units (IMUs) are provided to the software, and
based on the inertial data, the software determines position,
velocity, attitude, and other parameters used to navigate vehicles
such as missiles, aircraft, and spacecraft. The software associated
with the navigation systems has often undergone very extensive and
expensive verification and qualification--particularly for weapons
or aircraft systems--to ensure that the navigation system achieves
reliable navigation solutions as designed. The IMU is typically
external to the navigation processing system executing the software
and communicates the inertial measurement data to the software via
an IMU interface. Performing inertial measurement functions with a
device external to the navigation processing system affords system
designers some flexibility in their choice of, and replacement of,
IMUs for use in the systems because the IMU can be replaced without
the need to revalidate the navigation processing system's
software.
In order to update such navigation processing systems (hereinafter
referred to as "legacy" systems) to create potentially more
accurate systems, system designers find themselves having to add
additional hardware and create new interfaces for their legacy
systems, and significantly alter their existing software--all at
great delay and expense. This is because the legacy systems and
software are designed based on legacy IMU interfaces, which receive
inertial measurement data in the form of changes in velocity
(typically denoted by .DELTA.v) and changes in angle (typically
denoted by .DELTA..theta.). For example, adding a global navigation
satellite system (GNSS) receiver to a legacy system is one way to
obtain improved positioning solution performance. The problem is
that GNSS receivers output data in the form of position and
velocity rather than in the form of changes in velocity (.DELTA.v)
and changes in angle (.DELTA..theta.). Modification of the legacy
system's software to accommodate the GNSS receivers output data
would require re-verification and re-qualification of the
navigation processing systems. The added expenses for this redesign
and retesting tends to offset the possibility of achieving improved
performance at the relatively small cost of an added GNSS
receiver.
For the reasons stated above and for other reasons stated below
which will become apparent to those skilled in the art upon reading
and understanding the specification, there is a need in the art for
systems and methods for improved inertial navigation.
SUMMARY
The Embodiments of the present invention provide methods and
systems for systems and methods for improved inertial navigation
and will be understood by reading and studying the following
specification.
In one embodiment, a method for producing inertial measurement data
is provided. The method comprises receiving raw inertial
measurement data from one or more inertial sensors; receiving raw
position data based on signals from a global navigation satellite
system; processing the raw inertial measurement data and the raw
position data with a filter to generate state variable estimates;
and calculating enhanced inertial measurement data based on the raw
inertial measurement data and the state variable estimates from the
filter.
In another embodiment, a global navigation satellite system
enhanced inertial measurement unit is provided. The unit comprises
one or more inertial sensors adapted to detect a change in angular
position (.DELTA..theta.) and a change in velocity (.DELTA.v) and
generate raw inertial measurement data based on the detected change
in angular position and change in velocity; a global navigation
satellite system receiver adapted to generate raw position data
based on signals received from a global navigation satellite
system; a filter coupled to the one or more inertial sensors and
the global navigation satellite system receiver, the filter adapted
to calculate one or more estimated state variables including a
change to the change in velocity (.delta..DELTA.v) and a change in
the change in angular position (.delta..DELTA..theta.) based on the
raw inertial measurement data and the raw position data; and an
output generator adapted to calculate an enhanced inertial
measurement data based on the sum of the raw inertial measurement
data and the one or more estimated state variables.
In still another embodiment, a navigation system is provided. The
system comprises means for generating raw inertial measurement
data; means for generating raw position data; means for generating
state variable estimates responsive to the means for generating raw
inertial measurement data and the means for generating raw position
data; and means for calculating enhanced inertial measurement data
responsive to the means for generating raw inertial measurement
data and the means for generating state variable estimates.
DRAWINGS
Embodiments of the present invention can be more easily understood
and further advantages and uses thereof more readily apparent, when
considered in view of the description of the preferred embodiments
and the following figures in which:
FIG. 1 is a block diagram of a navigation system of one embodiment
of the present invention;
FIG. 2 is a block diagram of an output generator of one embodiment
of the present invention; and
FIG. 3 is a flow chart of a method of one embodiment of the present
invention.
In accordance with common practice, the various described features
are not drawn to scale but are drawn to emphasize features relevant
to the present invention. Reference characters denote like elements
throughout figures and text.
DETAILED DESCRIPTION
In the following detailed description, reference is made to the
accompanying drawings that form a part hereof, and in which is
shown by way of specific illustrative embodiments in which the
invention may be practiced. These embodiments are described in
sufficient detail to enable those skilled in the art to practice
the invention, and it is to be understood that other embodiments
may be utilized and that logical, mechanical and electrical changes
may be made without departing from the scope of the present
invention. The following detailed description is, therefore, not to
be taken in a limiting sense.
Embodiments of the present invention provide systems and methods
for an enhanced inertial measurement unit (IMU) that provides much
more accurate data than current IMUs and will interface with
external systems the same way as current IMUs. Standard IMU
interfaces provide output data formatted in terms of a change in
the IMU's angular position (.DELTA..theta.) and change in the IMU's
velocity (.DELTA.v). Embodiments of the present invention utilize
positioning data from a global navigation satellite system (GNSS)
receiver and inertial measurement data from inertial sensors to
develop enhanced precision .DELTA.v and .DELTA..theta. data for
legacy navigation systems designed to receive .DELTA.v and
.DELTA..theta. data. These legacy systems thus benefit from
enhanced precision inertial measurement data without the need to
modify any system software or input interfaces of the legacy
system. Embodiments of the present invention enable a GNSS enhanced
IMU that provides high precision inertial measurements through
existing IMU interfaces.
FIG. 1 is a block diagram illustrating a navigation system 105 for
a vehicle 100 of one embodiment of the present invention. In
alternate implementations, vehicle 100 comprises a vehicle such as,
but not limited to, a missile, satellite, aircraft, or other
vehicle that benefits from high precision positioning data for
navigation purposes. Such positioning data may include one or more
of vehicle 100's position, velocity and attitude. In one
embodiment, vehicle 100's position is expressed in terms of
latitude, longitude and altitude. In other embodiments, other
reference coordinate frameworks are used to express vehicle 100's
position. In one embodiment, vehicle 100's attitude is expressed in
terms of yaw, pitch and roll. In other embodiments, other reference
coordinate frameworks are used to express vehicle 100's attitude.
In the embodiment of FIG. 1, navigation system 105 provides the
positioning data to a guidance and propulsion system 106 which
includes any of those functionalities necessary (for example,
flaps, engines, thrusters, rockets) to maneuver vehicle 100 along a
desired trajectory.
Navigation system 105 includes a navigation processor 110 coupled
to a GNSS enhanced IMU 120 (referred to as GNSS/IMU 120) via an IMU
interface 112. In operation, navigation processor 110 receives
inertial measurement data from GNSS/IMU 120 via IMU interface 112
and based on that inertial measurement data, navigation software
114 calculates the position and velocity of vehicle 100 using one
or more algorithms known to those in the art of navigation systems.
In the embodiment shown in FIG. 1, navigation processor 110 is a
legacy navigation system designed to receive inertial measurement
data via IMU interface 112 in a form comprising change in angular
position data (.DELTA..theta.) and change in velocity data
(.DELTA.v).
GNSS/IMU 120 comprises one or more inertial sensors 122 a GNSS
receiver 124, a Kalman filter 126, and an output generator 128. In
one embodiment, inertial sensors 122 comprise a commercially
available IMU. In operation, inertial sensors 122 detect changes in
the angular position and velocity of vehicle 100 and output raw
inertial measurement data (shown as .DELTA.v and .DELTA..theta.)
based on the detected changes. In one embodiment, inertial sensors
122 include one or more gyroscopes and accelerometers to produce
the raw inertial measurement data. In one embodiment, GNSS receiver
124 comprises a global positioning system (GPS) receiver, a Galileo
system receiver, or similar device that calculates its own position
based on signals received from a GNSS. In operation, GNSS receiver
124 calculates it position based on signals received from a GNSS
and outputs raw position data (shown as P.sub.GPS). In alternate
embodiments, the raw position data provides vehicle 100's position
expressed in terms of latitude, longitude and altitude. In other
embodiments, GNSS receiver 124 uses other reference coordinate
frameworks to express vehicle 100's position.
Kalman filter 126 is coupled to inertial sensors 122 and GNSS
receiver 124, and inputs the raw inertial measurement data
(.DELTA.v, .DELTA..theta.) from inertial sensors 122 and the raw
position data (P.sub.GPS) from GNSS receiver 124. As would be
appreciated by one skilled in the art upon studying this
specification, a Kalman filter is a recursive filter which
estimates one or more state variables of a dynamic system from a
series of incomplete, noisy, or relatively imprecise measurements.
Although a Kalman filter is specifically discussed in this
specification, one of ordinary skill in the art upon reading this
specification would appreciate that embodiments of the present
invention are not limited to using Kalman filters but may use other
such filters to estimate the one or more state variables. Using
.DELTA.v, .DELTA..theta. and P.sub.GPS as measured input data,
Kalman filter 126 estimates the change occurring in the inertial
measurements .DELTA.v and .DELTA..theta., and from the estimates
outputs state variables .delta..DELTA.v and .delta..DELTA..theta.,
respectively. In one embodiment, Kalman filter 126 further
estimates the position of vehicle 100, and the rate of change in
position of vehicle 100, and outputs state variables (P.sub.EST)
and ({dot over (P)}.sub.EST), respectively.
Output generator 128 is coupled to inertial sensors 122 and Kalman
filter 126. Using the raw inertial measurement .DELTA.v from
inertial sensors 122, and the estimated state variable
.delta..DELTA.v calculated by Kalman filter 126, output generator
128 generates an enhanced inertial velocity measurement (shown as
.DELTA.v') based on the sum of .DELTA.v+.delta..DELTA.v. Similarly,
using the raw inertial measurement .DELTA..theta. from inertial
sensors 122, and the estimated state variable .delta..DELTA..theta.
calculated by Kalman filter 126, IMU output generator 128 generates
an enhanced inertial angular measurement (shown as .DELTA..theta.')
based on the sum of .DELTA..theta.+.delta..DELTA..theta.. The
enhanced inertial measurement data (.DELTA.v' and .THETA.')
provides inertial measurements in the form of incremental velocity
change and incremental angle change information expected by
navigation system 110, enabling navigation system 110 to produce
highly accurate positioning information for navigating vehicle
100.
FIG. 2 provides further details illustrating an output generator
228 of one embodiment of the present invention. Output generator
functions as described with respect to the output generator of FIG.
1 by utilizing raw inertial measurements from inertial sensors 122
and estimated state variables from Kalman filter 126 to produce
enhanced inertial measurements .DELTA.v' and .THETA.'.
As would be appreciated by one skilled in the art upon reading this
specification, the output data rate of inertial sensors 122 may be
different than the state variable update frequency provided by
Kalman filter 126. Under such circumstances the .DELTA.v' and
.DELTA..theta.' produced by simply summing the respective
.DELTA.v+.delta..DELTA.v and .DELTA..theta.+.delta..DELTA..theta.
values can produce data irregularities that will trigger transient
detectors in inertial navigation software 114 used to implement
navigation system 110. Output generator 228 avoids such
irregularities in .DELTA.v' and .DELTA..THETA.' through a
combination of data interpolation, extrapolation, and smoothing as
discussed below.
In one embodiment, output generator 228 receives the raw inertial
measurement data .DELTA.v and .DELTA..theta. from inertial sensors
122 at the inertial sensors 122's normal data output rate,
illustrated in FIG. 2 by .DELTA.v(f.sub.IMU) and
.DELTA..theta.(f.sub.IMU). Similarly, output generator 228 receives
estimated state variables .delta..DELTA.v and
.delta..DELTA..theta.from Kalman filter 126 at the state variable
update rate, illustrated in FIG. 2 by .delta..DELTA.v(f.sub.KF) and
.delta..DELTA..theta.(f.sub.KF). Output generator 228 further
receives a clock frequency signal (illustrated in FIG. 2 by
f.sub.IMU) from inertial sensors 122 to facilitate the
synchronization of the data received from inertial sensors 122 and
Kalman filter 126. Output generator 228 includes a smoothing
function 240 that receives the .delta..DELTA.v(f.sub.KF) and
.delta..DELTA..theta.(f.sub.KF) updates from Kalman filter 126
before they are respectively summed with .DELTA.v(f.sub.IMU) and
.DELTA..theta.(f.sub.IMU). Smoothing function 240 smoothes the
.delta..DELTA.v(f.sub.KF) and .delta..DELTA..theta.(f.sub.KF) data
by one or both of data interpolation and data extrapolation based
on the clock frequency signal f.sub.IMU from inertial sensors 122
to produce respective values .delta..DELTA.v and
.delta..DELTA..theta. having the same data output rate as inertial
sensors 122 (illustrated in FIG. 2 by .delta..DELTA.v(f.sub.IMU)
and .delta..DELTA..theta.(f.sub.IMU)). .DELTA.v(f.sub.IMU) and
.delta..DELTA.v(f.sub.IMU) are combined at summer 242 to produce
.DELTA.v(f.sub.IMU)+.delta..DELTA.v(f.sub.IMU), while
.DELTA..theta.(f.sub.IMU) and .delta..DELTA..theta.(f.sub.IMU) are
combined at summer 244 to produce
.DELTA..theta.(f.sub.IMU)+.delta..DELTA..theta.(f.sub.IMU).
In one embodiment, in order to further compensate for Kalman filter
126 latency when calculating .DELTA.v' and .DELTA..theta.' from
.DELTA.v(f.sub.IMU)+.delta..DELTA.v(f.sub.IMU) and
.DELTA..theta.(f.sub.IMU)+.delta..DELTA..theta.(f.sub.IMU),
respectively, output generator 228 calculates the difference in
vehicle 100's position when calculated from
.DELTA.v(f.sub.IMU)+.delta..DELTA.v(f.sub.IMU) and
.DELTA..theta.(f.sub.IMU)+.delta..DELTA..theta.(f.sub.IMU) versus
vehicle 100's position as calculated by Kalman filter 126. In such
an embodiment, output generator 228 also receives the estimate
state variables (P.sub.EST) and ({dot over (P)}.sub.EST) from
Kalman filter 126. For the reasons discussed above, the data rate
of the .DELTA.v(f.sub.IMU)+.delta..DELTA.v(f.sub.IMU) and
.DELTA..theta.(f.sub.IMU)+.delta..DELTA..theta.(f.sub.IMU) data can
be different from the update frequency at which Kalman filter 126
updates the values of P.sub.EST and {dot over (P)}.sub.EST.
Therefore, output generator 228 includes a smoothing function 250
which receives the P.sub.EST and {dot over (P)}.sub.EST state
variable output from Kalman filter 126 and, based on the clock
frequency signal f.sub.IMU from inertial sensors 122, smoothes the
P.sub.EST and {dot over (P)}.sub.EST state variable data by one or
both of data interpolation and data extrapolation to produce a
first estimate of vehicle 100's position that is updated at the
same rate as the data output rate of inertial sensors 122
(illustrated in FIG. 2 by P.sub.1(f.sub.IMU)). A second estimate of
vehicle 100's position is provided by trajectory estimator 260,
which receives the .DELTA.v(f.sub.IMU)+.delta..DELTA.v(f.sub.IMU)
output from summer 242 and the
.DELTA..theta.(f.sub.IMU)+.delta..DELTA..theta.(f.sub.IMU) output
from summer 244, and from these values calculates the second
estimated position of vehicle 100 (illustrated in FIG. 2 by
P2(f.sub.IMU)). The difference between the first estimated position
and the second estimated position (illustrated in FIG. 2 by
.DELTA.P) is at least partially a function of the latency of Kalman
filter 126. Based on .DELTA.P, lag adjustment function (LAF) 270
compensates for the latency by adjusting one or both of
.DELTA.v(f.sub.IMU)+.delta..DELTA.v(f.sub.IMU) and
.DELTA..theta.(f.sub.IMU)+.theta..DELTA..theta.(f.sub.IMU) to
produce the enhanced output .DELTA.v' and .DELTA..theta.',
respectively.
Referring back to FIG. 1, in one alternate embodiment, Kalman
filter 126 also provides its estimate for the rate of change in
position, {dot over (P)}.sub.EST, to GNSS receiver 124. As would be
appreciated by one skilled in the art upon reading this
specification, GNSS receivers are often designed to provide
improved position data when provided information about the
receivers velocity. In one embodiment, GNSS receiver 124 is such a
receiver and incorporates {dot over (P)}.sub.EST into its algorithm
for arriving at its position solution. Also, GNSS receivers are
also typically designed to provide a "quality of service" output
that represents the quality of the position solution it is
providing. For example, the quality of service output may indicate
that the position solution is of higher quality when the GNSS
receiver obtains its solution based on signals from a greater
number of GNSS satellites, and a position solution of lower quality
when GNSS receiver obtains its solution based on signals from a few
number of GNSS satellites. In one embodiment, GNSS receiver 124
provides a quality of service output to navigation processor 110.
In such an embodiment, navigation processor 110 weighs its reliance
on the enhanced inertial measurement data .DELTA.v' and
.DELTA..theta.' provided by GNSS/IMU 120 based on the quality of
service information from GNSS receiver 124. In one embodiment, when
the quality of service information from GNSS receiver 124
decreases, navigation processor 110 increases its reliance on one
or more other navigation aids 130 (for example, inertial data from
another IMU or position information from a star tracker) in
performing its navigation calculations. In another embodiment
Kalman filter 126 also produces an inertial sensor compensation
signal that is received by inertial sensors 122 and used by the
inertial sensors 122 to calibrate the accuracy of its raw inertial
measurement output .DELTA.v and .DELTA..theta..
FIG. 3 is a flow chart illustrating a method for producing inertial
measurement data of one embodiment of the present invention. The
method begins at 310 with receiving raw inertial measurement data
from one or more inertial sensors. In one embodiment, the one or
more inertial sensors include one or more gyroscopes and
accelerometers. In one embodiment, the raw inertial measurement
data includes changes in angular position (.DELTA..theta.) and
changes in velocity (.DELTA.v) detected by the inertial sensors.
The method proceeds to 320 with receiving raw position data based
on signals from a GNSS. In one embodiment, the signals from the
GNSS include one or both of signals from GPS satellites and signals
from Galileo satellites. In one embodiment the raw position data
includes latitude, longitude and altitude information. In other
embodiments, the raw position data includes positioning information
expressed in other reference coordinate frameworks. The method
proceeds to 330 with processing the raw inertial measurement data
and the raw position data with a Kalman filter to generate state
variable estimates. In one embodiment, the state variable estimates
include changes to inertial state variable .DELTA.v (i.e.,
.delta..DELTA.v) and inertial state variable .DELTA..theta. (i.e.,
.delta..DELTA..theta.). In one embodiment, the state variable
estimates further include a vehicle position (P.sub.EST) and a rate
of change in the vehicle position ({dot over (P)}.sub.EST). The
method proceeds to 340 with calculating enhanced inertial
measurement data based on a summation of the raw inertial
measurement data and the state variable estimates from the Kalman
filter. In one embodiment, calculating enhanced inertial
measurement data includes calculating an enhanced inertial velocity
measurement (.DELTA.v') based on the sum of .DELTA.v and
.delta..DELTA.v. In one embodiment, calculating enhanced inertial
measurement data includes calculating an enhanced inertial angular
measurement (.DELTA..theta.') based on the sum of .DELTA..theta.
and .delta..DELTA..theta.. The enhanced inertial measurement data
(.DELTA.v' and .DELTA..theta.') provides inertial measurement data
in the form of incremental velocity and angle information, which in
one embodiment may be utilized by legacy navigation systems or
other systems expecting inertial measurement data inputs, without
the need to modify any system software or input interfaces of the
legacy system.
Although specific embodiments have been illustrated and described
herein, it will be appreciated by those of ordinary skill in the
art that any arrangement, which is calculated to achieve the same
purpose, may be substituted for the specific embodiment shown. This
application is intended to cover any adaptations or variations of
the present invention. Therefore, it is manifestly intended that
this invention be limited only by the claims and the equivalents
thereof.
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